Whenever I go to choose a movie to watch, I’m always struck by the fact that the highest-ranking ones don’t appeal to me. I have been wondering if there was something wrong with my taste, but then Wired magazine recently revealed that IMDb’s top movie list allows algorithms to process the voting data – and over 70 percent of that data comes from men. The film site Rotten Tomatoes bases its data analysis on other reviews, but that data comes from a similarly large share of men. In other words, the algorithms that define the world’s best films do so according to men’s preferences. Movies that are ranked high by women do not have a chance at replacing The Godfather or The Dark Knight.

In the current and long-awaited debate on gender equality, it is interesting to reflect on what happens to gender roles when artificial intelligence interprets our data and helps us make decisions. New research from Princeton, published in the scientific journal Science, shows that algorithms to a large extent associate words such as “leadership” and “pay” with men, while words like “home” and “family” are more often connected to women.

With machine learning, when computers sort through large amounts of training data and learn by example, they are using long-ingrained stereotypes hidden in our everyday lives. Machines that are learning to understand human language start from the assumption that a word is best defined by its relationship with other words. Thus machines interpret the word “computer” as something that is related to men, and the word “handicraft” as something that is related to women. This statistical approach captures the cultural and social context of words in a way that reference books have never done – and the algorithm brings our human prejudice into the equation.

We are further contaminating these algorithms through our use of digital services. Most people who are given the task to design a shoe make a male boot and the majority of photographs depicting important occupations contain men. According to researchers at the University of Virginia, machines make gender associations with one third of all objects and that ratio is even higher when it comes to verbs. Google’s software translates gender-neutral pronouns from multiple languages to “he” when the text refers to doctors and “she” when it refers to nurses. It is statistically correct that more doctors are men, but not something that should be communicated as a given.

Microsoft has shown that machines trained on prejudiced original data do not only reflect gender discrimination. Their algorithms connect men with the word “programming” to an even greater extent than what the data actually shows.

When AI-based systems take on increasingly complicated tasks, the risks of automated decision-making increase. If a future kitchen robot serves a man a beer but helps a woman with the dishes, it’s certainly rather annoying. But when robots guide men and women to make different decisions concerning their educations, jobs and pension investments, the consequences become considerably more serious.

Ethical questions about what kind of world we want to teach the computers and how we can get our system to work in the desired direction are extremely relevant. Educational material for children often shows an idealized world, with female role models in traditionally male roles – and the opposite. In the same way, we need gender-conscious algorithms and a modern version of an equality ombudsman to ensure that different individuals are treated fairly. Otherwise there is a risk that the algorithm-driven world will have a far greater affect on us than a few bad film recommendations. We cannot allow the AI systems to confine us to outdated gender roles. Equality in the future must be significantly better than it has been in the past.

From a spider-like helmet on my head my alternating happy, sad and stressful thoughts are channeled to a screen on the wall – which is filled by growing graphs in different colors. Sensors in the helmet read EEG signals as indicators of my feelings. I’m testing Emotiv, which is one of the many new technologies that collect biometric data to measure emotional currents. Today, facial recognition software can pinpoint happiness if you smile into the camera. Our faces are one big field of expression that is easy for machines to learn. A heart bursting with feelings of love can be recorded by the heart rate monitor in a smart watch. Sweat secretions when someone is late for work can be captured by sensors that analyze stress levels.

The happiness of nations and customer’s feelings have long been evaluated with the help of surveys. Now there’s a whole host of new methods that, with input from bodily data, can calculate the most likely emotion at any given time. There is even software that analyzes social media activities, not just our word choices and their import, but also context and patterns. In other words, we have some rather adept happiness meters.

By moving from self-reported happiness in surveys to measuring actual feelings in real-time – and by connecting them with world events – research will come closer to real experiences. Harvard has conducted the study “Track Your Happiness” with the help of an app that measures life happiness. The Hedonometer project has charted happiness levels in US cities by analyzing 37 million tweets by 180,000 people. Now even the great technology giants are getting on the bandwagon. Apple recently invested in a company that measures emotional-based brainwaves and Mark Zuckerberg said last week that Facebook is developing software that can read thoughts and feelings in order to turn them into text. For real.

Technology that reveals our feelings can definitely be an invasion of privacy, but advertisers are rejoicing about how this will improve methods of measuring the effects of marketing. Customer service centers will be able to read customers’ moods using voice analysis, security police will get a new layer of information at airports and other sensitive sites, and it could even help people with autism to better interact with others. We are also going to start seeing products based on emotional data. Nikon is experimenting with a sensor camera that can read location, sound and temperature to customize the photo according to the emotional mood.

One of the most interesting application areas for emotional data would be to expand our rigid GDP measurements to include the value of social interactions that have previously not been measurable, such as friendship, family happiness, ethics and a sense of meaning in life. Data shows, for example, that we have the clearest feelings of happiness when we help others. Several nations talk about well-being as an important goal for sustainable community development. Can we calculate how much we actually take pleasure in parks, sporting events or in having a visible police force in the vicinity? A study in Amsterdam showed that a noise increase from the airport made people unhappier than a decrease in their own incomes.

It follows that in the future we will be able to provide informed answers to the question of how we feel. In my case, with the spider on my head, I could only watch the screen. Yes, thanks, the data says I feel totally fine. Hopefully, it will make us more aware of what contexts we are most likely to thrive in. The American Meteorological Society recently made an emotional map that shows that happiness is maximized at 57 degrees Fahrenheit. So enjoy spring before it gets too hot for happy days.

We’re seeing record growth in summer tourism worldwide. The Chinese are the ones who are getting around the most these days, surpassing Americans in travel statistics, and the United States reports that the average Chinese tourist is now spending more than anyone else. According to Burberry, 70 percent of visitors to their store in London are from China. The travel industry is ecstatic over these statistics, but what you don’t see in the visitor numbers is a new type of tourism that not only brings suitcases and deep pockets, but comes with a digital assistant.

Mobile bots are going to be able to assist with our trips from A to Z. The same virtual agent who books the trip can also, with the help of artificial intelligence, tailor a traveller’s experience at a destination. Services, excursions and restaurants can be automatically pre-booked based on data about the individual’s interests and tastes. The virtual agent then simply follows along on the trip, providing information about activities in the area, acting as guide at different attractions and shopping for merchandise that is collected by e-commerce and delivered directly to one’s home country.

In other words, artificial intelligence opens up possibilities for creating an exceptional travel experience, but also requires large amounts of data and advanced technology. At the same time, hotels, museums and transport operators are investing heavily – Starwood Hotels, Hilton and many more are building unique visitor apps – which says a lot about the fact that the future traveler’s demands for local services can only be answered by global technology platforms. It’s not easy to be a local boat owner who must automatically tailor trips to tourists without knowing who they are. Facebook, Google and similar players know this – and have become a familiar filter for travelers in foreign countries. What used to be an impenetrable jungle of local procedures, inaccessible services and complicated reservations in a different currency suddenly feels a lot like home. With new technology we are able to bring our habits along with us. We can have the menu read in our own language, bring payment apps and use our club memberships with loyalty points.

With the new stream of Chinese tourists comes the first evidence that travelers are not looking for the same local experiences as before. At Caesar’s Palace in Las Vegas it is possible to pay with WeChat, China’s largest social platform, at all of their hotels, restaurants and slot machines. Marriott has formed a joint venture with Alibaba, the Chinese e-commerce giant. Even Apple’s Siri is moving into the hotel chain Aloft, so guests have a familiar voice that regulates temperature and light in the room – and KLM is offering their tickets via Facebook.

Customer contact is being taken away from the travel industry, which risks becoming subcontractors of reasonably generic nights, tickets and transportation. It’s a shift that began when guests started using their own phones and downloading movies on their tablets instead of using the hotel phone and ordering pay-per-view movies in the room – and this transition is continuing at a fast pace. Takeout food can be easily ordered, laundry picked up, gym sessions and spa treatments booked from similar five star providers outside the hotel’s walls – all recommended by the assistant in the app, which accordingly holds the power.

Tourism is by definition mobile and with a virtual assistant every part of the travel industry is being transformed. But does artificial intelligence make traveling smarter? When every tourist is able to move across the world with a digital profile and an assistant in one’s luggage, the world becomes more accessible, but also more uniform. It becomes easier, more convenient and predictable. The risk is that we travel in a kind of isolated bubble that becomes a filter through which we view the world. It will feel just like being at home, even when you’re somewhere else.

In 1997, Reed Hastings envisioned a digital site where, for a fixed price, customers could watch an endless amount of movies. At that time the technical infrastructure didn’t exist and the viewing public, who had just become accustomed to DVDs, were simply not ready. But Hastings had a clear vision of the future, and he managed to start Netflix, albeit with a temporary business model. Using the good old-fashioned postal service, he rented out DVD movies via a subscription model. In 2011, when Hastings first dared to prioritize streaming and offer customers the opportunity to subscribe to only the digital service at a lower price, he was met by public outcry. 800,000 customers left in protest and the stock price dropped 77 percent in four months. In the long run, Hastings would prevail. In April 2017, Netflix reported about 93 million paying customers worldwide. Thanks to its temporary solution – and the courage to move on to the next business model – Netflix managed to successfully challenge both themselves and the market. They were perfectly prepared for the coming streaming explosion with a core of faithful users, large amounts of behavioral data, industry contacts and, perhaps most importantly, an excellent recommendation engine. Netflix created its own future market.

Robert Wolcott, Professor at the Kellogg School of Management, calls the phenomenon “temporary business ideas”: A company deliberately launches a business concept that cannot last – and uses that window of time to build a position and assets in anticipation of market changes.

Uber has clearly stated that their existing business model, based on freelance chauffeurs, is a temporary solution in expectation of self-driving cars. Google has invested in Nest in order to sneak a thermostat into the smart home of the future. PayPal’s founder, Peter Thiel, said in 1999 that in the future we would all be like mini-ATMs with digital wallets in our mobile phones, but at the time he could only launch a temporary payment solution based on credit cards where users could “beam” the program with their Palm Pilots. Today, over 200 million people use PayPal’s significantly more advanced mobile payment systems.

Fast-moving markets are difficult to assess. But by investing in temporary business models, prospective companies can be at the forefront of the next development stage. More start-ups than established giants invest in temporary solutions. It seems that younger companies are more likely to put energy into understanding the user needs and market structure of the future. For those companies in the midst of digital transformation, this can be an important lesson. Mark Zuckerberg annually convenes all of his employees and partners to present a detailed 10-year vision of how the market will evolve and what role Facebook will play in it. Is he always right? Certainly not, but this provides direction and instills the courage necessary to create temporary business models that will lead to the next success.

What is the difference between a temporary business model and ordinary experimentation? It’s about deliberately creating an in-road to an expected market. It demands an understanding of the direction forward to make the right investment – and the flexibility to adapt as reality unfolds.

Many companies are currently stuck in business models that have served them well for many years, but which are not watertight solutions for tomorrow’s market. If the giant step into the digitally transparent ecosystem of the future is too complicated, perhaps a contemporary version of Hasting’s DVD rental is what could save them.

The debate about robots taking our jobs away sometimes sounds like an automation apocalypse. Yuval Harari, the author of Homo Deus, believes that we soon will have a “useless class” who cannot work at all, and researchers report that it is possible to automate half of all jobs. The fact that robots and artificial intelligence can replace our livelihoods is scary. Unemployment is the question we now worry most about in the world, according to a survey by Ipsos.

However, we should focus more on reinventing professions than discussing those that will disappear. According to a study by the McKinsey Global Institute, it is necessary to restore the “rationalized” workforce to value-added professions at the same rate that we drive automation. Competitiveness is increasing and public spending can be streamlined by automation, but it’s not enough to maintain our desired growth rate. For companies, the efficiency gains are obvious; for society as a whole, the effects are more complicated. As the population grows older, the dependency ratio is increasing at a rapid pace. We cannot afford to put people out of work.

It’s hard to predict what the jobs of the future will be, but we have to start piecing the puzzle together. We know that there will be a great demand for the kind of digital competence that makes computers and robots do what we want, as well as a continued demand for creative and caring professions that can still demand skills that computers (as yet) cannot. All other jobs will have less value. According to Gartner, 45 percent of the fastest growing companies will soon have more smart machines than employees. We will need to learn to work side by side with the robots. In Sweden, we already have problems with an educational system that does not keep pace with the changes in the world. According to UKÄ (the Swedish Higher Education Authority), only one third of the areas of study in Sweden have a balance between labor market demand and the number of graduates. And the impact of automation still has not hit with full force. (For example, the Authority does not recognize that there is a lack of so-called “computer studies” students, though the rest of the world is screaming for them).

We can also prepare ourselves by increasing the flexibility and furthering the education of the labor force. Self-employed people in a gig economy, where digital service platforms can effectively get supply and demand to meet, may be the buffer we need for a more fluidlabor market. Perhaps we need a national program for the upgrading of competencies, which would be based on the increasing availability of data on how professions arise, change and disappear. In Australia, it is estimated that within 2-5 years 90 percent of the labor force will need fewer, simpler skills, while 50 percent will need more advanced technological ones. Companies can be incentivized to take greater responsibility for creating employable employees. In the US, AT&T has made an enormous contribution to society by sending all 247,000 employees to training in data analysis.

Concerns about unemployment can easily be translated into protectionist solutions that provide the illusion of making a country “great again” – but such solutions are not built for the future. Sweden has many strengths to offer the future labor market. Our free universities will be a definite asset once they are re-organized to better balance the need for new skills and offer programs for lifelong learning. If we continue to build on our history of award-winning expertise in the areas of design and creative services using technology, and with new, flexible working methods, a culture based on collaborative problem-solving, and good old-fashioned teamwork rather than hierarchical disciplinary thinking, we will be even more unique.